Online Bridge Structural Condition Assessment Based on the Gaussian Process: A Representative Data Selection and Performance Warning Strategy

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Structural Control & Health Monitoring Pub Date : 2024-03-07 DOI:10.1155/2024/5579734
Yi-Chen Zhu, Yun-Wen Zheng, Wen Xiong, Jiang-Xin Li, C. S. Cai, Chao Jiang
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Abstract

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.

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基于高斯过程的在线桥梁结构状况评估:代表性数据选择和性能预警策略
由于拥有大量结构和运行状况数据的传感器技术的快速发展,数据驱动方法现已广泛应用于土木基础设施的结构健康监测。数据驱动方法的一个主要问题是,计算时间会随着监测数据数量的增加而增加,这限制了其在在线结构状态评估中的应用。本文以桥梁结构健康监测为重点,提出了一种基于高斯过程模型的在线性能评估代表性数据选择策略。所提出的方法可以有效减少训练所需的监测数据量,从而使桥梁性能评估可以实时进行。该方法采用概率论方式开发,可严格考虑桥梁监测数据的相关不确定性。提出了一种用于桥梁状况评估和异常检测的概率预警指数。利用合成数据对所提出的方法进行了验证,并将其应用于两座全尺寸桥梁的结构状态评估,说明了实际应用的可行性。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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